Construct Non-Hierarchical P/NBD Model for Online Retail Transaction Data
In this workbook we construct our first hierarchical P/NBD models on the synthetic data with the longer timeframe.
1 Load and Construct Datasets
We start by modelling the P/NBD model using our synthetic datasets before we try to model real-life data.
1.1 Load Online Retail Data
We now want to load the online retail transaction data.
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customer_cohortdata_tbl <- read_rds("data/onlineretail_cohort_tbl.rds")
customer_cohortdata_tbl |> glimpse()Rows: 5,852
Columns: 5
$ customer_id <chr> "12346", "12347", "12348", "12349", "12350", "12351", …
$ cohort_qtr <chr> "2010 Q1", "2010 Q4", "2010 Q3", "2010 Q2", "2011 Q1",…
$ cohort_ym <chr> "2010 03", "2010 10", "2010 09", "2010 04", "2011 02",…
$ first_tnx_date <date> 2010-03-02, 2010-10-31, 2010-09-27, 2010-04-29, 2011-…
$ total_tnx_count <int> 3, 8, 5, 3, 1, 1, 9, 2, 1, 2, 6, 2, 5, 10, 6, 4, 10, 2…Show code
customer_transactions_tbl <- read_rds("data/onlineretail_transactions_tbl.rds")
customer_transactions_tbl |> glimpse()Rows: 36,658
Columns: 4
$ tnx_timestamp <dttm> 2009-12-01 07:45:00, 2009-12-01 07:45:59, 2009-12-01 09…
$ customer_id <chr> "13085", "13085", "13078", "15362", "18102", "12682", "1…
$ invoice_id <chr> "489434", "489435", "489436", "489437", "489438", "48943…
$ tnx_amount <dbl> 505.30, 145.80, 630.33, 310.75, 2286.24, 372.30, 50.40, …Show code
customer_subset_id <- read_rds("data/onlineretail_customer_subset_ids.rds")
customer_subset_id |> glimpse() chr [1:2000] "12347" "12348" "12349" "12355" "12357" "12359" "12360" ...1.2 Load Derived Data
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id_1000 <- read_rds("data/longsynth_id_1000.rds")
id_5000 <- read_rds("data/longsynth_id_5000.rds")
id_10000 <- read_rds("data/longsynth_id_10000.rds")
fit_1000_data_tbl <- read_rds("data/longsynth_fit_1000_data_tbl.rds")
fit_10000_data_tbl <- read_rds("data/longsynth_fit_10000_data_tbl.rds")
customer_fit_stats_tbl <- fit_1000_data_tbl
customer_summarystats_tbl <- read_rds("data/longsynth_customer_summarystats_tbl.rds")
obs_fitdata_tbl <- read_rds("data/longsynth_obs_fitdata_tbl.rds")
obs_validdata_tbl <- read_rds("data/longsynth_obs_validdata_tbl.rds")
customer_subset_id <- id_10001.3 Load Subset Data
We also want to construct our data subsets for the purposes of speeding up our valuations.
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customer_fit_subset_tbl <- obs_fitdata_tbl |>
filter(customer_id %in% customer_subset_id)
customer_fit_subset_tbl |> glimpse()Rows: 1,000
Columns: 6
$ customer_id <chr> "LFC201001_0016", "LFC201001_0027", "LFC201001_0094", "…
$ first_tnx_date <dttm> 2010-01-02 04:57:52, 2010-01-03 07:02:01, 2010-01-09 2…
$ last_tnx_date <dttm> 2010-01-02 04:57:52, 2010-03-17 11:30:13, 2010-03-12 1…
$ tnx_count <dbl> 0, 4, 1, 0, 5, 2, 1, 38, 0, 0, 0, 0, 10, 10, 4, 2, 23, …
$ t_x <dbl> 0.0000000, 10.4551784, 8.8501005, 0.0000000, 5.9899357,…
$ T_cal <dbl> 625.9704, 625.8153, 624.8741, 624.7492, 624.7605, 623.8…Show code
customer_valid_subset_tbl <- obs_validdata_tbl |>
filter(customer_id %in% customer_subset_id)
customer_valid_subset_tbl |> glimpse()Rows: 1,000
Columns: 3
$ customer_id <chr> "LFC201001_0016", "LFC201001_0027", "LFC201001_0094"…
$ tnx_count <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
$ tnx_last_interval <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …We now use these datasets to set the start and end dates for our various validation methods.
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dates_lst <- read_rds("data/onlineretail_simulation_dates.rds")
use_fit_start_date <- dates_lst$use_fit_start_date
use_fit_end_date <- dates_lst$use_fit_end_date
use_valid_start_date <- dates_lst$use_valid_start_date
use_valid_end_date <- dates_lst$use_valid_end_dateFinally, we need to set our directories where we save our Stan code and the model outputs.
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stan_modeldir <- "stan_models"
stan_codedir <- "stan_code"2 Fit First P/NBD Model
We now construct our Stan model and prepare to fit it with our synthetic dataset.
We also want to set a number of overall parameters for this workbook
To start the fit data, we want to use the 1,000 customers. We also need to calculate the summary statistics for the validation period.
2.1 Compile and Fit Stan Model
We now compile this model using CmdStanR.
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pnbd_fixed_stanmodel <- cmdstan_model(
"stan_code/pnbd_fixed.stan",
include_paths = stan_codedir,
pedantic = TRUE,
dir = stan_modeldir
)We then use this compiled model with our data to produce a fit of the data.
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stan_modelname <- "pnbd_onlineretail_fixed1"
stanfit_seed <- stanfit_seed + 1
stanfit_prefix <- str_c("fit_", stan_modelname)
stanfit_object_file <- glue("data/{stanfit_prefix}_stanfit.rds")
stan_data_lst <- customer_fit_stats_tbl |>
select(customer_id, x, t_x, T_cal) |>
compose_data(
lambda_mn = 0.25,
lambda_cv = 1.00,
mu_mn = 0.10,
mu_cv = 1.00,
)
if(!file_exists(stanfit_object_file)) {
pnbd_onlineretail_fixed1_stanfit <- pnbd_fixed_stanmodel$sample(
data = stan_data_lst,
chains = 4,
iter_warmup = 500,
iter_sampling = 500,
seed = stanfit_seed,
save_warmup = TRUE,
output_dir = stan_modeldir,
output_basename = stanfit_prefix,
)
pnbd_onlineretail_fixed1_stanfit$save_object(stanfit_object_file, compress = "gzip")
} else {
pnbd_onlineretail_fixed1_stanfit <- read_rds(stanfit_object_file)
}
pnbd_onlineretail_fixed1_stanfit$summary()# A tibble: 3,001 × 10
variable mean median sd mad q5 q95 rhat ess_bulk
<chr> <num> <num> <num> <num> <num> <num> <num> <num>
1 lp__ -2.51e+4 -2.51e+4 34.6 34.8 -2.52e+4 -2.51e+4 1.00 696.
2 lambda[1] 1.42e-1 8.46e-2 0.167 0.0954 5.18e-3 4.87e-1 1.00 2900.
3 lambda[2] 2.92e-1 2.74e-1 0.135 0.130 1.10e-1 5.38e-1 1.00 3770.
4 lambda[3] 1.09e-1 8.77e-2 0.0844 0.0694 1.67e-2 2.71e-1 1.00 2583.
5 lambda[4] 1.41e-1 8.40e-2 0.166 0.0958 4.07e-3 4.78e-1 1.00 2968.
6 lambda[5] 5.19e-1 4.85e-1 0.235 0.219 2.04e-1 9.61e-1 1.00 4224.
7 lambda[6] 3.01e-1 2.67e-1 0.187 0.164 7.62e-2 6.55e-1 0.999 4169.
8 lambda[7] 2.56e-1 1.96e-1 0.211 0.160 3.40e-2 6.87e-1 1.00 3941.
9 lambda[8] 2.97e-1 2.93e-1 0.0496 0.0477 2.19e-1 3.83e-1 1.00 4867.
10 lambda[9] 1.43e-1 8.19e-2 0.176 0.0952 4.37e-3 4.92e-1 1.00 3036.
# ℹ 2,991 more rows
# ℹ 1 more variable: ess_tail <num>We have some basic HMC-based validity statistics we can check.
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pnbd_onlineretail_fixed1_stanfit$cmdstan_diagnose()Processing csv files: /home/rstudio/btydwork/stan_models/fit_pnbd_onlineretail_fixed1-1.csvWarning: non-fatal error reading adaptation data
, /home/rstudio/btydwork/stan_models/fit_pnbd_onlineretail_fixed1-2.csvWarning: non-fatal error reading adaptation data
, /home/rstudio/btydwork/stan_models/fit_pnbd_onlineretail_fixed1-3.csvWarning: non-fatal error reading adaptation data
, /home/rstudio/btydwork/stan_models/fit_pnbd_onlineretail_fixed1-4.csvWarning: non-fatal error reading adaptation data
Checking sampler transitions treedepth.
Treedepth satisfactory for all transitions.
Checking sampler transitions for divergences.
No divergent transitions found.
Checking E-BFMI - sampler transitions HMC potential energy.
E-BFMI satisfactory.
Effective sample size satisfactory.
Split R-hat values satisfactory all parameters.
Processing complete, no problems detected.2.2 Visual Diagnostics of the Sample Validity
Now that we have a sample from the posterior distribution we need to create a few different visualisations of the diagnostics.
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parameter_subset <- c(
"lambda[1]", "lambda[2]", "lambda[3]", "lambda[4]",
"mu[1]", "mu[2]", "mu[3]", "mu[4]"
)
pnbd_onlineretail_fixed1_stanfit$draws(inc_warmup = FALSE) |>
mcmc_trace(pars = parameter_subset) +
expand_limits(y = 0) +
labs(
x = "Iteration",
y = "Value",
title = "Traceplot of Sample of Lambda and Mu Values"
) +
theme(axis.text.x = element_text(size = 10))
We also check \(N_{eff}\) as a quick diagnostic of the fit.
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pnbd_onlineretail_fixed1_stanfit |>
neff_ratio(pars = c("lambda", "mu")) |>
as.numeric() |>
mcmc_neff() +
ggtitle("Plot of Parameter Effective Sample Sizes")
2.3 Assess the Model
As we intend to run the same logic to assess each of our models, we have combined all this logic into a single function run_model_assessment, to run the simulations and combine the datasets.
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pnbd_stanfit <- pnbd_onlineretail_fixed1_stanfit |>
recover_types(customer_fit_stats_tbl)
pnbd_onlineretail_fixed1_assess_data_lst <- run_model_assessment(
model_stanfit = pnbd_stanfit,
insample_tbl = customer_fit_subset_tbl,
fit_label = "pnbd_onlineretail_fixed1",
fit_end_dttm = use_fit_end_date |> as.POSIXct(),
valid_start_dttm = use_valid_start_date |> as.POSIXct(),
valid_end_dttm = use_valid_end_date |> as.POSIXct(),
sim_seed = 10
)
pnbd_onlineretail_fixed1_assess_data_lst |> glimpse()List of 5
$ model_fit_index_filepath : 'glue' chr "data/pnbd_onlineretail_fixed1_assess_fit_index_tbl.rds"
$ model_valid_index_filepath : 'glue' chr "data/pnbd_onlineretail_fixed1_assess_valid_index_tbl.rds"
$ model_simstats_filepath : 'glue' chr "data/pnbd_onlineretail_fixed1_assess_model_simstats_tbl.rds"
$ model_fit_simstats_filepath : 'glue' chr "data/pnbd_onlineretail_fixed1_assess_fit_simstats_tbl.rds"
$ model_valid_simstats_filepath: 'glue' chr "data/pnbd_onlineretail_fixed1_assess_valid_simstats_tbl.rds"2.3.1 Check In-Sample Data Validation
We first check the model against the in-sample data.
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simdata_tbl <- pnbd_onlineretail_fixed1_assess_data_lst |>
use_series(model_fit_simstats_filepath) |>
read_rds()
insample_plots_lst <- create_model_assessment_plots(
obsdata_tbl = obs_fitdata_tbl,
simdata_tbl = simdata_tbl
)
insample_plots_lst$multi_plot |> print()
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insample_plots_lst$total_plot |> print()
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insample_plots_lst$quant_plot |> print()
This fit looks reasonable and appears to capture most of the aspects of the data used to fit it. Given that this is a synthetic dataset, this is not surprising, but at least we appreciate that our model is valid.
2.3.2 Check Out-of-Sample Data Validation
We now repeat for the out-of-sample data.
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simdata_tbl <- pnbd_onlineretail_fixed1_assess_data_lst |>
use_series(model_valid_simstats_filepath) |>
read_rds()
outsample_plots_lst <- create_model_assessment_plots(
obsdata_tbl = obs_validdata_tbl,
simdata_tbl = simdata_tbl
)
outsample_plots_lst$multi_plot |> print()
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outsample_plots_lst$total_plot |> print()
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outsample_plots_lst$quant_plot |> print()
As for our short time frame data, overall our model is working well.
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 3807489 203.4 6554545 350.1 6554545 350.1
Vcells 25254283 192.7 66858214 510.1 66856627 510.13 Fit Alternate Prior Model.
We want to try an alternate prior model with a smaller co-efficient of variation to see what impact it has on our procedures.
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stan_modelname <- "pnbd_onlineretail_fixed2"
stanfit_seed <- stanfit_seed + 1
stanfit_prefix <- str_c("fit_", stan_modelname)
stanfit_object_file <- glue("data/{stanfit_prefix}_stanfit.rds")
stan_data_lst <- customer_fit_stats_tbl |>
select(customer_id, x, t_x, T_cal) |>
compose_data(
lambda_mn = 0.25,
lambda_cv = 0.50,
mu_mn = 0.10,
mu_cv = 0.50,
)
if(!file_exists(stanfit_object_file)) {
pnbd_onlineretail_fixed2_stanfit <- pnbd_fixed_stanmodel$sample(
data = stan_data_lst,
chains = 4,
iter_warmup = 500,
iter_sampling = 500,
seed = stanfit_seed,
save_warmup = TRUE,
output_dir = stan_modeldir,
output_basename = stanfit_prefix,
)
pnbd_onlineretail_fixed2_stanfit$save_object(stanfit_object_file, compress = "gzip")
} else {
pnbd_onlineretail_fixed2_stanfit <- read_rds(stanfit_object_file)
}
pnbd_onlineretail_fixed2_stanfit$summary()# A tibble: 3,001 × 10
variable mean median sd mad q5 q95 rhat ess_bulk
<chr> <num> <num> <num> <num> <num> <num> <num> <num>
1 lp__ -4.30e+4 -4.30e+4 32.4 33.5 -4.30e+4 -4.29e+4 1.00 721.
2 lambda[1] 2.11e-1 1.93e-1 0.115 0.106 6.11e-2 4.28e-1 0.999 3454.
3 lambda[2] 2.74e-1 2.60e-1 0.102 0.0973 1.33e-1 4.70e-1 0.999 5023.
4 lambda[3] 1.76e-1 1.64e-1 0.0824 0.0762 6.38e-2 3.28e-1 1.00 3733.
5 lambda[4] 2.15e-1 1.94e-1 0.115 0.102 6.90e-2 4.34e-1 1.00 3904.
6 lambda[5] 3.76e-1 3.61e-1 0.130 0.125 1.91e-1 6.06e-1 1.01 4985.
7 lambda[6] 2.69e-1 2.54e-1 0.115 0.111 1.15e-1 4.78e-1 1.00 3949.
8 lambda[7] 2.49e-1 2.31e-1 0.115 0.113 9.19e-2 4.72e-1 1.00 4803.
9 lambda[8] 2.94e-1 2.91e-1 0.0456 0.0420 2.23e-1 3.78e-1 1.00 5182.
10 lambda[9] 2.11e-1 1.91e-1 0.112 0.104 6.90e-2 4.20e-1 1.00 4317.
# ℹ 2,991 more rows
# ℹ 1 more variable: ess_tail <num>We have some basic HMC-based validity statistics we can check.
Show code
pnbd_onlineretail_fixed2_stanfit$cmdstan_diagnose()Processing csv files: /home/rstudio/btydwork/stan_models/fit_pnbd_onlineretail_fixed2-1.csvWarning: non-fatal error reading adaptation data
, /home/rstudio/btydwork/stan_models/fit_pnbd_onlineretail_fixed2-2.csvWarning: non-fatal error reading adaptation data
, /home/rstudio/btydwork/stan_models/fit_pnbd_onlineretail_fixed2-3.csvWarning: non-fatal error reading adaptation data
, /home/rstudio/btydwork/stan_models/fit_pnbd_onlineretail_fixed2-4.csvWarning: non-fatal error reading adaptation data
Checking sampler transitions treedepth.
Treedepth satisfactory for all transitions.
Checking sampler transitions for divergences.
No divergent transitions found.
Checking E-BFMI - sampler transitions HMC potential energy.
E-BFMI satisfactory.
Effective sample size satisfactory.
Split R-hat values satisfactory all parameters.
Processing complete, no problems detected.3.1 Visual Diagnostics of the Sample Validity
Now that we have a sample from the posterior distribution we need to create a few different visualisations of the diagnostics.
Show code
parameter_subset <- c(
"lambda[1]", "lambda[2]", "lambda[3]", "lambda[4]",
"mu[1]", "mu[2]", "mu[3]", "mu[4]"
)
pnbd_onlineretail_fixed2_stanfit$draws(inc_warmup = FALSE) |>
mcmc_trace(pars = parameter_subset) +
expand_limits(y = 0) +
labs(
x = "Iteration",
y = "Value",
title = "Traceplot of Sample of Lambda and Mu Values"
) +
theme(axis.text.x = element_text(size = 10))
We want to check the \(N_{eff}\) statistics also.
Show code
pnbd_onlineretail_fixed2_stanfit |>
neff_ratio(pars = c("lambda", "mu")) |>
as.numeric() |>
mcmc_neff() +
ggtitle("Plot of Parameter Effective Sample Sizes")
3.2 Assess the Model
As we intend to run the same logic to assess each of our models, we have combined all this logic into a single function run_model_assessment, to run the simulations and combine the datasets.
Show code
pnbd_stanfit <- pnbd_onlineretail_fixed2_stanfit |>
recover_types(customer_fit_stats_tbl)
pnbd_onlineretail_fixed2_assess_data_lst <- run_model_assessment(
model_stanfit = pnbd_stanfit,
insample_tbl = customer_fit_subset_tbl,
fit_label = "pnbd_onlineretail_fixed2",
fit_end_dttm = use_fit_end_date |> as.POSIXct(),
valid_start_dttm = use_valid_start_date |> as.POSIXct(),
valid_end_dttm = use_valid_end_date |> as.POSIXct(),
sim_seed = 20
)
pnbd_onlineretail_fixed2_assess_data_lst |> glimpse()List of 5
$ model_fit_index_filepath : 'glue' chr "data/pnbd_onlineretail_fixed2_assess_fit_index_tbl.rds"
$ model_valid_index_filepath : 'glue' chr "data/pnbd_onlineretail_fixed2_assess_valid_index_tbl.rds"
$ model_simstats_filepath : 'glue' chr "data/pnbd_onlineretail_fixed2_assess_model_simstats_tbl.rds"
$ model_fit_simstats_filepath : 'glue' chr "data/pnbd_onlineretail_fixed2_assess_fit_simstats_tbl.rds"
$ model_valid_simstats_filepath: 'glue' chr "data/pnbd_onlineretail_fixed2_assess_valid_simstats_tbl.rds"3.2.1 Check In-Sample Data Validation
We first check the model against the in-sample data.
Show code
simdata_tbl <- pnbd_onlineretail_fixed2_assess_data_lst |>
use_series(model_fit_simstats_filepath) |>
read_rds()
insample_plots_lst <- create_model_assessment_plots(
obsdata_tbl = obs_fitdata_tbl,
simdata_tbl = simdata_tbl
)
insample_plots_lst$multi_plot |> print()
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insample_plots_lst$total_plot |> print()
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insample_plots_lst$quant_plot |> print()
This fit looks reasonable and appears to capture most of the aspects of the data used to fit it. Given that this is a synthetic dataset, this is not surprising, but at least we appreciate that our model is valid.
3.2.2 Check Out-of-Sample Data Validation
We now repeat for the out-of-sample data.
Show code
simdata_tbl <- pnbd_onlineretail_fixed2_assess_data_lst |>
use_series(model_valid_simstats_filepath) |>
read_rds()
outsample_plots_lst <- create_model_assessment_plots(
obsdata_tbl = obs_validdata_tbl,
simdata_tbl = simdata_tbl
)
outsample_plots_lst$multi_plot |> print()
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outsample_plots_lst$total_plot |> print()
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outsample_plots_lst$quant_plot |> print()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 3832721 204.7 6554545 350.1 6554545 350.1
Vcells 37318195 284.8 80312016 612.8 80310291 612.84 Fit Tight-Lifetime Model
We now want to try a model where we use priors with a tighter coefficient of variation for lifetime but keep the CoV for transaction frequency.
Show code
stan_modelname <- "pnbd_onlineretail_fixed3"
stanfit_seed <- stanfit_seed + 1
stanfit_prefix <- str_c("fit_", stan_modelname)
stanfit_object_file <- glue("data/{stanfit_prefix}_stanfit.rds")
stan_data_lst <- customer_fit_stats_tbl |>
select(customer_id, x, t_x, T_cal) |>
compose_data(
lambda_mn = 0.25,
lambda_cv = 1.00,
mu_mn = 0.10,
mu_cv = 0.50,
)
if(!file_exists(stanfit_object_file)) {
pnbd_onlineretail_fixed3_stanfit <- pnbd_fixed_stanmodel$sample(
data = stan_data_lst,
chains = 4,
iter_warmup = 500,
iter_sampling = 500,
seed = stanfit_seed,
save_warmup = TRUE,
output_dir = stan_modeldir,
output_basename = stanfit_prefix,
)
pnbd_onlineretail_fixed3_stanfit$save_object(stanfit_object_file, compress = "gzip")
} else {
pnbd_onlineretail_fixed3_stanfit <- read_rds(stanfit_object_file)
}
pnbd_onlineretail_fixed3_stanfit$summary()# A tibble: 3,001 × 10
variable mean median sd mad q5 q95 rhat ess_bulk
<chr> <num> <num> <num> <num> <num> <num> <num> <num>
1 lp__ -3.55e+4 -3.55e+4 33.7 34.3 -3.55e+4 -3.54e+4 1.00 655.
2 lambda[1] 1.40e-1 8.60e-2 0.162 0.0960 5.65e-3 4.36e-1 0.999 3134.
3 lambda[2] 2.98e-1 2.75e-1 0.147 0.137 1.06e-1 5.79e-1 1.00 5744.
4 lambda[3] 1.16e-1 9.07e-2 0.0912 0.0716 1.86e-2 2.97e-1 1.00 3773.
5 lambda[4] 1.37e-1 8.35e-2 0.156 0.0953 4.91e-3 4.53e-1 1.00 3118.
6 lambda[5] 5.18e-1 4.85e-1 0.228 0.211 2.09e-1 9.40e-1 1.00 4708.
7 lambda[6] 2.93e-1 2.51e-1 0.184 0.164 6.99e-2 6.42e-1 1.00 3378.
8 lambda[7] 2.55e-1 2.03e-1 0.205 0.162 3.46e-2 6.62e-1 1.00 3740.
9 lambda[8] 2.97e-1 2.94e-1 0.0493 0.0481 2.21e-1 3.84e-1 1.00 6602.
10 lambda[9] 1.44e-1 8.68e-2 0.175 0.0952 4.82e-3 5.01e-1 1.00 3240.
# ℹ 2,991 more rows
# ℹ 1 more variable: ess_tail <num>We have some basic HMC-based validity statistics we can check.
Show code
pnbd_onlineretail_fixed3_stanfit$cmdstan_diagnose()Processing csv files: /home/rstudio/btydwork/stan_models/fit_pnbd_onlineretail_fixed3-1.csvWarning: non-fatal error reading adaptation data
, /home/rstudio/btydwork/stan_models/fit_pnbd_onlineretail_fixed3-2.csvWarning: non-fatal error reading adaptation data
, /home/rstudio/btydwork/stan_models/fit_pnbd_onlineretail_fixed3-3.csvWarning: non-fatal error reading adaptation data
, /home/rstudio/btydwork/stan_models/fit_pnbd_onlineretail_fixed3-4.csvWarning: non-fatal error reading adaptation data
Checking sampler transitions treedepth.
Treedepth satisfactory for all transitions.
Checking sampler transitions for divergences.
No divergent transitions found.
Checking E-BFMI - sampler transitions HMC potential energy.
E-BFMI satisfactory.
Effective sample size satisfactory.
Split R-hat values satisfactory all parameters.
Processing complete, no problems detected.4.1 Visual Diagnostics of the Sample Validity
Now that we have a sample from the posterior distribution we need to create a few different visualisations of the diagnostics.
Show code
parameter_subset <- c(
"lambda[1]", "lambda[2]", "lambda[3]", "lambda[4]",
"mu[1]", "mu[2]", "mu[3]", "mu[4]"
)
pnbd_onlineretail_fixed3_stanfit$draws(inc_warmup = FALSE) |>
mcmc_trace(pars = parameter_subset) +
expand_limits(y = 0) +
labs(
x = "Iteration",
y = "Value",
title = "Traceplot of Sample of Lambda and Mu Values"
) +
theme(axis.text.x = element_text(size = 10))
We want to check the \(N_{eff}\) statistics also.
Show code
pnbd_onlineretail_fixed3_stanfit |>
neff_ratio(pars = c("lambda", "mu")) |>
as.numeric() |>
mcmc_neff() +
ggtitle("Plot of Parameter Effective Sample Sizes")
4.2 Assess the Model
As we intend to run the same logic to assess each of our models, we have combined all this logic into a single function run_model_assessment, to run the simulations and combine the datasets.
Show code
pnbd_stanfit <- pnbd_onlineretail_fixed3_stanfit |>
recover_types(customer_fit_stats_tbl)
pnbd_onlineretail_fixed3_assess_data_lst <- run_model_assessment(
model_stanfit = pnbd_stanfit,
insample_tbl = customer_fit_subset_tbl,
fit_label = "pnbd_onlineretail_fixed3",
fit_end_dttm = use_fit_end_date |> as.POSIXct(),
valid_start_dttm = use_valid_start_date |> as.POSIXct(),
valid_end_dttm = use_valid_end_date |> as.POSIXct(),
sim_seed = 30
)
pnbd_onlineretail_fixed3_assess_data_lst |> glimpse()List of 5
$ model_fit_index_filepath : 'glue' chr "data/pnbd_onlineretail_fixed3_assess_fit_index_tbl.rds"
$ model_valid_index_filepath : 'glue' chr "data/pnbd_onlineretail_fixed3_assess_valid_index_tbl.rds"
$ model_simstats_filepath : 'glue' chr "data/pnbd_onlineretail_fixed3_assess_model_simstats_tbl.rds"
$ model_fit_simstats_filepath : 'glue' chr "data/pnbd_onlineretail_fixed3_assess_fit_simstats_tbl.rds"
$ model_valid_simstats_filepath: 'glue' chr "data/pnbd_onlineretail_fixed3_assess_valid_simstats_tbl.rds"4.2.1 Check In-Sample Data Validation
We first check the model against the in-sample data.
Show code
simdata_tbl <- pnbd_onlineretail_fixed3_assess_data_lst |>
use_series(model_fit_simstats_filepath) |>
read_rds()
insample_plots_lst <- create_model_assessment_plots(
obsdata_tbl = obs_fitdata_tbl,
simdata_tbl = simdata_tbl
)
insample_plots_lst$multi_plot |> print()
Show code
insample_plots_lst$total_plot |> print()
Show code
insample_plots_lst$quant_plot |> print()
This fit looks reasonable and appears to capture most of the aspects of the data used to fit it. Given that this is a synthetic dataset, this is not surprising, but at least we appreciate that our model is valid.
4.2.2 Check Out-of-Sample Data Validation
We now repeat for the out-of-sample data.
Show code
simdata_tbl <- pnbd_onlineretail_fixed3_assess_data_lst |>
use_series(model_valid_simstats_filepath) |>
read_rds()
outsample_plots_lst <- create_model_assessment_plots(
obsdata_tbl = obs_validdata_tbl,
simdata_tbl = simdata_tbl
)
outsample_plots_lst$multi_plot |> print()
Show code
outsample_plots_lst$total_plot |> print()
Show code
outsample_plots_lst$quant_plot |> print()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 3857466 206.1 6554545 350.1 6554545 350.1
Vcells 49380877 376.8 115827824 883.7 96456520 736.05 Fit Narrow-Short-Lifetime Model
We now want to try a model where we use priors with a tighter coefficient of variation for lifetime but keep the CoV for transaction frequency.
Show code
stan_modelname <- "pnbd_onlineretail_fixed4"
stanfit_seed <- stanfit_seed + 1
stanfit_prefix <- str_c("fit_", stan_modelname)
stanfit_object_file <- glue("data/{stanfit_prefix}_stanfit.rds")
stan_data_lst <- customer_fit_stats_tbl |>
select(customer_id, x, t_x, T_cal) |>
compose_data(
lambda_mn = 0.25,
lambda_cv = 1.00,
mu_mn = 0.20,
mu_cv = 0.30,
)
if(!file_exists(stanfit_object_file)) {
pnbd_onlineretail_fixed4_stanfit <- pnbd_fixed_stanmodel$sample(
data = stan_data_lst,
chains = 4,
iter_warmup = 500,
iter_sampling = 500,
seed = stanfit_seed,
save_warmup = TRUE,
output_dir = stan_modeldir,
output_basename = stanfit_prefix,
)
pnbd_onlineretail_fixed4_stanfit$save_object(stanfit_object_file, compress = "gzip")
} else {
pnbd_onlineretail_fixed4_stanfit <- read_rds(stanfit_object_file)
}
pnbd_onlineretail_fixed4_stanfit$summary()# A tibble: 3,001 × 10
variable mean median sd mad q5 q95 rhat ess_bulk
<chr> <num> <num> <num> <num> <num> <num> <num> <num>
1 lp__ -5.24e+4 -5.24e+4 33.0 33.1 -5.25e+4 -5.24e+4 1.02 575.
2 lambda[1] 1.59e-1 9.34e-2 0.182 0.105 6.37e-3 5.62e-1 1.00 3069.
3 lambda[2] 3.06e-1 2.83e-1 0.142 0.134 1.16e-1 5.64e-1 1.01 4019.
4 lambda[3] 1.26e-1 1.03e-1 0.0939 0.0821 2.05e-2 3.10e-1 1.00 3892.
5 lambda[4] 1.60e-1 1.02e-1 0.180 0.107 7.61e-3 4.98e-1 1.00 2699.
6 lambda[5] 5.31e-1 4.96e-1 0.233 0.227 2.16e-1 9.81e-1 1.00 4544.
7 lambda[6] 3.22e-1 2.81e-1 0.196 0.171 8.08e-2 6.95e-1 1.00 4085.
8 lambda[7] 2.79e-1 2.25e-1 0.222 0.176 3.77e-2 7.14e-1 1.00 3782.
9 lambda[8] 2.97e-1 2.96e-1 0.0459 0.0452 2.27e-1 3.77e-1 1.01 4301.
10 lambda[9] 1.57e-1 1.03e-1 0.171 0.111 7.44e-3 4.94e-1 1.00 2401.
# ℹ 2,991 more rows
# ℹ 1 more variable: ess_tail <num>We have some basic HMC-based validity statistics we can check.
Show code
pnbd_onlineretail_fixed4_stanfit$cmdstan_diagnose()Processing csv files: /home/rstudio/btydwork/stan_models/fit_pnbd_onlineretail_fixed4-1.csvWarning: non-fatal error reading adaptation data
, /home/rstudio/btydwork/stan_models/fit_pnbd_onlineretail_fixed4-2.csvWarning: non-fatal error reading adaptation data
, /home/rstudio/btydwork/stan_models/fit_pnbd_onlineretail_fixed4-3.csvWarning: non-fatal error reading adaptation data
, /home/rstudio/btydwork/stan_models/fit_pnbd_onlineretail_fixed4-4.csvWarning: non-fatal error reading adaptation data
Checking sampler transitions treedepth.
Treedepth satisfactory for all transitions.
Checking sampler transitions for divergences.
No divergent transitions found.
Checking E-BFMI - sampler transitions HMC potential energy.
E-BFMI satisfactory.
Effective sample size satisfactory.
Split R-hat values satisfactory all parameters.
Processing complete, no problems detected.5.1 Visual Diagnostics of the Sample Validity
Now that we have a sample from the posterior distribution we need to create a few different visualisations of the diagnostics.
Show code
parameter_subset <- c(
"lambda[1]", "lambda[2]", "lambda[3]", "lambda[4]",
"mu[1]", "mu[2]", "mu[3]", "mu[4]"
)
pnbd_onlineretail_fixed4_stanfit$draws(inc_warmup = FALSE) |>
mcmc_trace(pars = parameter_subset) +
expand_limits(y = 0) +
labs(
x = "Iteration",
y = "Value",
title = "Traceplot of Sample of Lambda and Mu Values"
) +
theme(axis.text.x = element_text(size = 10))
We want to check the \(N_{eff}\) statistics also.
Show code
pnbd_onlineretail_fixed4_stanfit |>
neff_ratio(pars = c("lambda", "mu")) |>
as.numeric() |>
mcmc_neff() +
ggtitle("Plot of Parameter Effective Sample Sizes")
5.2 Assess the Model
As we intend to run the same logic to assess each of our models, we have combined all this logic into a single function run_model_assessment, to run the simulations and combine the datasets.
Show code
pnbd_stanfit <- pnbd_onlineretail_fixed4_stanfit |>
recover_types(customer_fit_stats_tbl)
pnbd_onlineretail_fixed4_assess_data_lst <- run_model_assessment(
model_stanfit = pnbd_stanfit,
insample_tbl = customer_fit_subset_tbl,
fit_label = "pnbd_onlineretail_fixed4",
fit_end_dttm = use_fit_end_date |> as.POSIXct(),
valid_start_dttm = use_valid_start_date |> as.POSIXct(),
valid_end_dttm = use_valid_end_date |> as.POSIXct(),
sim_seed = 40
)
pnbd_onlineretail_fixed4_assess_data_lst |> glimpse()List of 5
$ model_fit_index_filepath : 'glue' chr "data/pnbd_onlineretail_fixed4_assess_fit_index_tbl.rds"
$ model_valid_index_filepath : 'glue' chr "data/pnbd_onlineretail_fixed4_assess_valid_index_tbl.rds"
$ model_simstats_filepath : 'glue' chr "data/pnbd_onlineretail_fixed4_assess_model_simstats_tbl.rds"
$ model_fit_simstats_filepath : 'glue' chr "data/pnbd_onlineretail_fixed4_assess_fit_simstats_tbl.rds"
$ model_valid_simstats_filepath: 'glue' chr "data/pnbd_onlineretail_fixed4_assess_valid_simstats_tbl.rds"5.2.1 Check In-Sample Data Validation
We first check the model against the in-sample data.
Show code
simdata_tbl <- pnbd_onlineretail_fixed4_assess_data_lst |>
use_series(model_fit_simstats_filepath) |>
read_rds()
insample_plots_lst <- create_model_assessment_plots(
obsdata_tbl = obs_fitdata_tbl,
simdata_tbl = simdata_tbl
)
insample_plots_lst$multi_plot |> print()
Show code
insample_plots_lst$total_plot |> print()
Show code
insample_plots_lst$quant_plot |> print()
This fit looks reasonable and appears to capture most of the aspects of the data used to fit it. Given that this is a synthetic dataset, this is not surprising, but at least we appreciate that our model is valid.
5.2.2 Check Out-of-Sample Data Validation
We now repeat for the out-of-sample data.
Show code
simdata_tbl <- pnbd_onlineretail_fixed4_assess_data_lst |>
use_series(model_valid_simstats_filepath) |>
read_rds()
outsample_plots_lst <- create_model_assessment_plots(
obsdata_tbl = obs_validdata_tbl,
simdata_tbl = simdata_tbl
)
outsample_plots_lst$multi_plot |> print()
Show code
outsample_plots_lst$total_plot |> print()
Show code
outsample_plots_lst$quant_plot |> print()
used (Mb) gc trigger (Mb) max used (Mb)
Ncells 3882231 207.4 6554545 350.1 6554545 350.1
Vcells 61443578 468.8 115827824 883.7 115827661 883.76 Compare Model Outputs
We have looked at each of the models individually, but it is also worth looking at each of the models as a group.
Show code
calculate_simulation_statistics <- function(file_rds) {
simdata_tbl <- read_rds(file_rds)
multicount_cust_tbl <- simdata_tbl |>
filter(sim_tnx_count > 0) |>
count(draw_id, name = "multicust_count")
totaltnx_data_tbl <- simdata_tbl |>
count(draw_id, wt = sim_tnx_count, name = "simtnx_count")
simstats_tbl <- multicount_cust_tbl |>
inner_join(totaltnx_data_tbl, by = "draw_id")
return(simstats_tbl)
}Show code
obs_fit_customer_count <- obs_fitdata_tbl |>
filter(tnx_count > 0) |>
nrow()
obs_valid_customer_count <- obs_validdata_tbl |>
filter(tnx_count > 0) |>
nrow()
obs_fit_total_count <- obs_fitdata_tbl |>
pull(tnx_count) |>
sum()
obs_valid_total_count <- obs_validdata_tbl |>
pull(tnx_count) |>
sum()
obs_stats_tbl <- tribble(
~assess_type, ~name, ~obs_value,
"fit", "multicust_count", obs_fit_customer_count,
"fit", "simtnx_count", obs_fit_total_count,
"valid", "multicust_count", obs_valid_customer_count,
"valid", "simtnx_count", obs_valid_total_count
)
model_assess_tbl <- dir_ls("data", regexp = "pnbd_onlineretail_fixed.*_assess_.*simstats") |>
enframe(name = NULL, value = "file_path") |>
filter(str_detect(file_path, "_assess_model_", negate = TRUE)) |>
mutate(
model_label = str_replace(file_path, "data/pnbd_onlineretail_(.*?)_assess_.*", "\\1"),
assess_type = if_else(str_detect(file_path, "_assess_fit_"), "fit", "valid"),
sim_data = map(
file_path, calculate_simulation_statistics,
.progress = "calculate_simulation_statistics"
)
)
model_assess_tbl |> glimpse()Rows: 8
Columns: 4
$ file_path <fs::path> "data/pnbd_onlineretail_fixed1_assess_fit_simstats_tb…
$ model_label <chr> "fixed1", "fixed1", "fixed2", "fixed2", "fixed3", "fixed3"…
$ assess_type <chr> "fit", "valid", "fit", "valid", "fit", "valid", "fit", "va…
$ sim_data <list> [<tbl_df[2000 x 3]>], [<tbl_df[2000 x 3]>], [<tbl_df[2000…We have now constructed the simulation summary statistics and now reshape our data to aid in our model assessment.
Show code
model_assess_summstat_tbl <- model_assess_tbl |>
select(model_label, assess_type, sim_data) |>
unnest(sim_data) |>
pivot_longer(
cols = !c(model_label, assess_type, draw_id)
) |>
group_by(model_label, assess_type, name) |>
summarise(
.groups = "drop",
mean_val = mean(value),
p10 = quantile(value, 0.10),
p25 = quantile(value, 0.25),
p50 = quantile(value, 0.50),
p75 = quantile(value, 0.75),
p90 = quantile(value, 0.90)
)
model_assess_summstat_tbl |> glimpse()Rows: 16
Columns: 9
$ model_label <chr> "fixed1", "fixed1", "fixed1", "fixed1", "fixed2", "fixed2"…
$ assess_type <chr> "fit", "fit", "valid", "valid", "fit", "fit", "valid", "va…
$ name <chr> "multicust_count", "simtnx_count", "multicust_count", "sim…
$ mean_val <dbl> 50.2350, 249.3000, 53.1105, 928.2575, 56.0095, 222.9570, 3…
$ p10 <dbl> 45.0, 204.0, 48.0, 806.9, 50.0, 187.0, 35.0, 410.0, 44.0, …
$ p25 <dbl> 48.00, 225.00, 51.00, 863.00, 53.00, 203.00, 37.00, 450.00…
$ p50 <dbl> 50.0, 249.0, 53.0, 925.5, 56.0, 222.0, 40.0, 500.0, 50.0, …
$ p75 <dbl> 53.00, 272.00, 56.00, 992.25, 59.00, 242.00, 42.00, 549.00…
$ p90 <dbl> 55.0, 295.0, 58.0, 1053.0, 61.0, 260.0, 44.0, 597.1, 55.0,…We now use this data to construct model comparison plots for the different models we have fit.
Show code
#! echo: TRUE
ggplot(model_assess_summstat_tbl) +
geom_errorbar(
aes(x = model_label, ymin = p10, ymax = p90), width = 0
) +
geom_errorbar(
aes(x = model_label, ymin = p25, ymax = p75), width = 0, linewidth = 3
) +
geom_hline(
aes(yintercept = obs_value),
data = obs_stats_tbl, colour = "red"
) +
scale_y_continuous(labels = label_comma()) +
expand_limits(y = 0) +
facet_wrap(
vars(assess_type, name), scale = "free_y"
) +
labs(
x = "Model",
y = "Count",
title = "Comparison Plot for the Different Models"
) +
theme(
axis.text.x = element_text(angle = 20, vjust = 0.5, size = 8)
)
6.1 Write Assessment Data to Disk
We now want to save the assessment data to disk.
Show code
model_assess_tbl |> write_rds("data/assess_data_pnbd_onlineretail_fixed_tbl.rds")7 R Environment
Show code
options(width = 120L)
sessioninfo::session_info()─ Session info ───────────────────────────────────────────────────────────────────────────────────────────────────────
setting value
version R version 4.2.3 (2023-03-15)
os Ubuntu 22.04.2 LTS
system x86_64, linux-gnu
ui X11
language (EN)
collate en_US.UTF-8
ctype en_US.UTF-8
tz Europe/Dublin
date 2023-08-22
pandoc 2.19.2 @ /usr/local/bin/ (via rmarkdown)
─ Packages ───────────────────────────────────────────────────────────────────────────────────────────────────────────
package * version date (UTC) lib source
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arrayhelpers 1.1-0 2020-02-04 [1] RSPM (R 4.2.0)
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base64enc 0.1-3 2015-07-28 [1] RSPM (R 4.2.0)
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boot 1.3-28.1 2022-11-22 [2] CRAN (R 4.2.3)
bridgesampling 1.1-2 2021-04-16 [1] RSPM (R 4.2.0)
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cli 3.6.1 2023-03-23 [1] RSPM (R 4.2.0)
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[1] /usr/local/lib/R/site-library
[2] /usr/local/lib/R/library
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options(width = 80L)